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Mahipal
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Each rewritten description now states both what the skill does (concrete capability, named tools/artifacts) and an explicit when-to-use trigger, improving agent discovery/activation. Grounded in each skill's own body; changes confined to the `description` field only (bodies and all other frontmatter untouched). Produced by a gated audit->rewrite->recheck loop (548 -> 0 flagged) with a sampled anti-invention check (0 ungrounded). Schema: 817/817 pass. Framework-ID gate: 0 defects.
67 lines
3.2 KiB
Markdown
67 lines
3.2 KiB
Markdown
---
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name: analyzing-cobaltstrike-malleable-c2-profiles
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description: Parse and analyze Cobalt Strike Malleable C2 profiles with dissect.cobaltstrike (profiles and beacon-payload configs) and pyMalleableC2 (AST parsing) to extract HTTP/DNS transforms, URIs, headers, sleep/jitter, and injection behavior, then generate network detection signatures. Use when reverse-engineering a captured malleable profile or building detections against Cobalt Strike Beacon traffic.
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domain: cybersecurity
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subdomain: malware-analysis
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tags:
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- cobalt-strike
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- malleable-c2
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- c2-detection
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- beacon-analysis
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- network-signatures
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- threat-hunting
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- red-team-tools
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version: '1.0'
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author: mahipal
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license: Apache-2.0
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nist_csf:
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- DE.AE-02
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- RS.AN-03
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- ID.RA-01
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- DE.CM-01
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mitre_attack:
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- T1071.001
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- T1573.002
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- T1001.003
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- T1090.004
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- T1102
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---
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# Analyzing CobaltStrike Malleable C2 Profiles
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## Overview
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Cobalt Strike Malleable C2 profiles are domain-specific language scripts that customize how Beacon communicates with the team server, defining HTTP request/response transformations, sleep intervals, jitter values, user agents, URI paths, and process injection behavior. Threat actors use malleable profiles to disguise C2 traffic as legitimate services (Amazon, Google, Slack). Analyzing these profiles reveals network indicators for detection: URI patterns, HTTP headers, POST/GET transforms, DNS settings, and process injection techniques. The `dissect.cobaltstrike` library can parse both profile files and extract configurations from beacon payloads, while `pyMalleableC2` provides AST-based parsing using Lark grammar for programmatic profile manipulation and validation.
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## When to Use
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- When investigating security incidents that require analyzing cobaltstrike malleable c2 profiles
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- When building detection rules or threat hunting queries for this domain
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- When SOC analysts need structured procedures for this analysis type
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- When validating security monitoring coverage for related attack techniques
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## Prerequisites
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- Python 3.9+ with `dissect.cobaltstrike` and/or `pyMalleableC2`
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- Sample Malleable C2 profiles (available from public repositories)
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- Understanding of HTTP protocol and Cobalt Strike beacon communication model
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- Network monitoring tools (Suricata/Snort) for signature deployment
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- PCAP analysis tools for traffic validation
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## Steps
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1. Install libraries: `pip install dissect.cobaltstrike` or `pip install pyMalleableC2`
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2. Parse profile with `C2Profile.from_path("profile.profile")`
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3. Extract HTTP GET/POST block configurations (URIs, headers, parameters)
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4. Identify user agent strings and spoof targets
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5. Extract sleep time, jitter percentage, and DNS beacon settings
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6. Analyze process injection settings (spawn-to, allocation technique)
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7. Generate Suricata/Snort signatures from extracted network indicators
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8. Compare profile against known threat actor profile collections
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9. Extract staging URIs and payload delivery mechanisms
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10. Produce detection report with IOCs and recommended network signatures
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## Expected Output
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A JSON report containing extracted C2 URIs, HTTP headers, user agents, sleep/jitter settings, process injection config, spawned process paths, DNS settings, and generated Suricata-compatible detection rules.
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